#!/usr/bin/env python3
# -*- coding: utf-8 -*-

"""
能源系统智能化模块

功能：
1. 能源需求预测与分析
2. 智能电网优化调度
3. 可再生能源整合策略
"""

import torch
import torch.nn as nn
import pandas as pd
from sklearn.preprocessing import MinMaxScaler

class EnergySystemAI:
    """能源系统智能化核心类"""
    
    def __init__(self):
        # 示例模型结构
        self.demand_model = nn.Sequential(
            nn.Linear(24, 64),
            nn.ReLU(),
            nn.Linear(64, 32),
            nn.ReLU(),
            nn.Linear(32, 24)
        )
        self.scaler = MinMaxScaler()
        
    def forecast_demand(self, historical_data):
        """
        预测未来能源需求
        
        参数:
            historical_data: 历史能源消耗数据 (DataFrame)
            
        返回:
            预测结果 (DataFrame)
        """
        # 数据预处理
        scaled_data = self.scaler.transform(historical_data)
        
        # 模型推理
        # 示例: 使用简单神经网络进行预测
        inputs = torch.FloatTensor(scaled_data[-24:]).unsqueeze(0)
        with torch.no_grad():
            prediction = self.demand_model(inputs)
        
        # 反归一化
        return pd.DataFrame(
            self.scaler.inverse_transform(prediction.numpy()),
            columns=historical_data.columns
        )
    
    def optimize_grid(self, generation, consumption):
        """
        智能电网优化调度
        
        参数:
            generation: 发电数据 (dict)
            consumption: 用电数据 (dict)
            
        返回:
            优化调度方案 (dict)
        """
        # 示例: 简单的供需平衡算法
        balance = {
            'renewable_usage': min(generation['renewable'], consumption['total']),
            'traditional_usage': max(0, consumption['total'] - generation['renewable'])
        }
        return balance
    
    def integrate_renewables(self, weather_data):
        """
        可再生能源整合策略
        
        参数:
            weather_data: 天气预报数据 (dict)
            
        返回:
            整合建议 (str)
        """
        # 示例: 基于天气的可再生能源发电预测
        solar_potential = weather_data.get('solar_radiation', 0)
        wind_potential = weather_data.get('wind_speed', 0)
        
        return f"建议分配: 太阳能 {solar_potential*100:.1f}%, 风能 {wind_potential*10:.1f}%"